Dynamic VM Consolidation Enhancement for Designing and Evaluation of Energy Efficiency in Green Data Centers Using Regression Analysis

  • Authors

    • A V. Sajitha
    • A C. Subhajini
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.6.14966
  • Cloud computing, green cloud computing, data center, virtual machine placement, dynamic VM consolidation, VM live migration, linear regression.
  • Abstract

    Enhancement of dynamic Virtual Machines (VM) consolidation is an efficient means to improve the energy efficiency via effective resources utilization in Cloud data centers. In this paper, we propose an algorithm, Energy Conscious Greeny Cloud Dynamic Algorithm, which considers multiple factors such as CPU, memory and bandwidth utilization of the node for empowering VM consolidation by using regression analysis model. This algorithm is the combination of several adaptive algorithms such as EnCoReAn (UPReAn) for Predicting the Utility of a host), Overload and Under-load detection), VM Selection and Allocation algorithms, which helps to achieve live VM migration by switching-off unused servers to low-power mode (i.e., sleep or hibernation), thus saves energy and efficient resource utilization. This approach reduces the operational cost, computation time and increase the scalability. The experimental result proves that, the proposed algorithm attains significant percentage in reduction of energy consumption rather than existing VM consolidation strategies.

     

  • References

    1. [1] Jansen W & Grance T, “Guidelines on Security and Privacy in Public Cloud Computingâ€, National Institute of Standards and Technology, (2011), pp.800-144.

      [2] Garcıa PA, Fernández JMM, Rodrigo JLA & Buyya R, “Proactive Power and Thermal Aware Optimizations for Energy-Efficient Cloud Computingâ€, Escuela Tecnica Superior De Ingenieros De Telecomunicacion, (2017).

      [3] EC-European Commission. (2007). Limiting Global Climate Change to 2 degrees Celsius. The way ahead for 2020 and beyond. COM/2007/2.

      [4] Murugesan S & Gangadharan GR, Harnessing Green IT: Principles and Practices, Wiley Publishing, (2012).

      [5] Buyya R, Broberg J & Goscinski AM, Cloud Computing: Principles and Paradigms, John Wiley & Sons, (2010).

      [6] Abali B, Canturk I, Jeffrey OK, Suzanne KM & Dipankar S, Live Virtual Machine Migration Quality of Service, U.S. Patent Application, Vol.15, (2017).

      [7] Abdullah M, Lu K, Wieder P & Yahyapour R, “A Heuristic-Based Approach for Dynamic VMs Consolidation in Cloud Data Centersâ€, Arabian Journal for Science and Engineering, (2017), pp.1-15.

      [8] Quanwang W, Fuyuki I, Qingsheng Z & Yunni X, “Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacentersâ€, IEEE Transactions on Services Computing, (2016).

      [9] Georgia K, Emmanouil S, Amir AS & Polychronis K, “Can Everybody be Happy in the Cloud? Delay, Profit and Energy-Efficient Scheduling for Cloud Servicesâ€, Journal of Parallel and Distributed Computing, Vol.96, (2016), pp.202-217.

      [10] Mehiar D, Bechir H, Mohsen G & Ammar R, “An Energy-Efï¬cient VM Prediction and Migration Framework for Overcommitted Cloudsâ€, IEEE Transactions on Cloud Computing, Vol.7, No.4, (2017).

      [11] Zhe H & Danny HKT, “M-Convex VM Consolidation: Towards a Better VM Workload Consolidationâ€, IEEE Transactions on Cloud Computing, Vol.4, No.4, (2016).

      [12] Fahimeh F, Tapio P, Pasi L, Juha P, Nguyen TH & Hannu T, “Energy-aware VM Consolidation in Cloud Data Centers Using Utilization Prediction Modelâ€, IEEE Transactions on Cloud Computing, (2016).

      [13] Michael P, Gavriil T & Alex, D, “Decentralized and Energy-Efficient Workload Management in Enterprise Cloudsâ€, IEEE Transactions On Cloud Computing, Vol.4, No.2, (2016).

      [14] Mohammad AK, Mohd ND, Azizol A, Shamala S & Mohamed O, “Energy-Efficient Algorithms for Dynamic Virtual Machine Consolidation in Cloud Data Centersâ€, IEEE Transactions on Green Cloud and Fog Computing: Energy Efficient and Sustainable Infrastructures, Protocols and Applications, Vol.5, No.69, (2017), pp.10709–10722.

      [15] Jungmin S, Amir VD, Rodrigo NC & Rajkumar B, “SLA-aware and Energy-Efficient Dynamic Overbooking in SDN-Based Cloud Data Centersâ€, IEEE Transactions on Sustainable Computing, Vol.2, No.2, (2017).

      [16] Yang Y, Xiaolin C, Jiqiang L & Lin L, “Towards Robust Green Virtual Cloud Data Center Provisioningâ€, IEEE Transactions on Cloud Computing, (2017).

      [17] Nguyen TH, Di FM & Yla-Jaaski A, “Virtual Machine Consolidation with Multiple Usage Prediction for Energy-Efficient Cloud Data Centersâ€, IEEE Transactions on Services Computing, (2017).

      [18] Yi H, Jeffrey C, Tansu A & Christopher L, “Using Virtual Machine Allocation Policies to Defend against Co-resident Attacks in Cloud Computingâ€, IEEE Transactions On Dependable and Secure Computing, (2015).

      [19] Dan K, Sasa M, Matej G & Ondrej P, “Testing Internet Applications and Services Using PlanetLabâ€, Computer Standards & Interfaces, Vol.53, (2017), pp.33-38.

      [20] Rodrigo NC, Rajiv R, Anton B, Cesar AF, De R & Rajkumar B, “CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithmsâ€, Software: Practice and Experience, Vol.41, No.1, (2015), pp.23– 50.

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  • How to Cite

    V. Sajitha, A., & C. Subhajini, A. (2018). Dynamic VM Consolidation Enhancement for Designing and Evaluation of Energy Efficiency in Green Data Centers Using Regression Analysis. International Journal of Engineering & Technology, 7(3.6), 179-186. https://doi.org/10.14419/ijet.v7i3.6.14966

    Received date: 2018-07-02

    Accepted date: 2018-07-02

    Published date: 2018-07-04